{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling time series data is an important preprocessing step when using neural forecasting methods for several reasons:\n",
    "\n",
    "1. **Convergence speed**: Neural forecasting models tend to converge faster when the features are on a similar scale.\n",
    "2. **Avoiding vanishing or exploding gradients**: some architectures, such as recurrent neural networks (RNNs), are sensitive to the scale of input data. If the input values are too large, it could lead to exploding gradients, where the gradients become too large and the model becomes unstable. Conversely, very small input values could lead to vanishing gradients, where weight updates during training are negligible and the training fails to converge.\n",
    "3. **Ensuring consistent scale**: Neural forecasting models have shared global parameters for the all time series of the task. In cases where time series have different scale, scaling ensures that no particular time series dominates the learning process.\n",
    "4. **Improving generalization**: time series with consistent scale can lead to smoother loss surfaces. Moreover, scaling helps to homogenize the distribution of the input data, which can also improve generalization by avoiding out-of-range values.\n",
    "\n",
    "The `Neuralforecast` library integrates two types of temporal scaling:\n",
    "\n",
    "* **Time Series Scaling**: scaling each time series using all its data on the train set before start training the model. This is done by using the `local_scaler_type` parameter of the `Neuralforecast` core class.\n",
    "* **Window scaling (TemporalNorm)**: scaling each input window separetly for each element of the batch at every training iteration. This is done by using the `scaler_type` parameter of each model class.\n",
    "\n",
    "In this notebook, we will demonstrate how to scale the time series data with both methods on an Eletricity Price Forecasting (EPF) task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Time_Series_Scaling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install `Neuralforecast`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast\n",
    "!pip install hyperopt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` dataframe contains the target and exogenous variables past information to train the model. The `unique_id` column identifies the markets, `ds` contains the datestamps, and `y` the electricity price. For future variables, we include a forecast of how much electricity will be produced (`gen_forecast`), system load (`system_laod`), and day of week (`week_day`). Both the electricity system demand and offer impact the price significantly, including these variables to the model greatly improve performance, as we demonstrate in Olivares et al. (2022).\n",
    "\n",
    "The `futr_df` dataframe includes the information of the future exogenous variables for the period we want to forecast (in this case, 24 hours after the end of the train dataset `df`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>system_load</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>53.48</td>\n",
       "      <td>76905.0</td>\n",
       "      <td>74812.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>51.93</td>\n",
       "      <td>75492.0</td>\n",
       "      <td>71469.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>48.76</td>\n",
       "      <td>74394.0</td>\n",
       "      <td>69642.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>42.27</td>\n",
       "      <td>72639.0</td>\n",
       "      <td>66704.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>38.41</td>\n",
       "      <td>69347.0</td>\n",
       "      <td>65051.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      y  gen_forecast  system_load  week_day\n",
       "0        FR 2015-01-01 00:00:00  53.48       76905.0      74812.0         3\n",
       "1        FR 2015-01-01 01:00:00  51.93       75492.0      71469.0         3\n",
       "2        FR 2015-01-01 02:00:00  48.76       74394.0      69642.0         3\n",
       "3        FR 2015-01-01 03:00:00  42.27       72639.0      66704.0         3\n",
       "4        FR 2015-01-01 04:00:00  38.41       69347.0      65051.0         3"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv')\n",
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "\n",
    "futr_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_futr.csv')\n",
    "futr_df['ds'] = pd.to_datetime(futr_df['ds'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that `y` and the exogenous variables are on largely different scales. Next, we show two methods to scale the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Time Series Scaling with `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most widely used approches for scaling time series is to treat it as a pre-processing step, where each time series and temporal exogenous variables are scaled based on their entire information in the train set. Models are then trained on the scaled data.\n",
    "\n",
    "To simplify pipelines, we added a scaling functionality to the `Neuralforecast` class. Each time series will be scaled before training the model with either `fit` or `cross_validation`, and scaling statistics are stored. The class then uses the stored statistics to scale the forecasts back to the original scale before returning the forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a. Instantiate model and `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will use the `TimesNet` model, recently proposed in [Wu, Haixu, et al. (2022)](https://arxiv.org/abs/2210.02186). First instantiate the model with the desired parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.models import TimesNet\n",
    "from neuralforecast.core import NeuralForecast\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "model = TimesNet(h = horizon,                                                 # Horizon\n",
    "                 input_size = 5*horizon,                                      # Length of input window\n",
    "                 max_steps = 100,                                             # Training iterations\n",
    "                 top_k = 3,                                                   # Number of periods (for FFT).\n",
    "                 num_kernels = 3,                                             # Number of kernels for Inception module\n",
    "                 batch_size = 2,                                              # Number of time series per batch\n",
    "                 windows_batch_size = 32,                                     # Number of windows per batch\n",
    "                 learning_rate = 0.001,                                       # Learning rate\n",
    "                 futr_exog_list = ['gen_forecast', 'system_load','week_day'], # Future exogenous variables\n",
    "                 scaler_type = None)                                          # We use the Core scaling method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object and using the `fit` method. The `local_scaler_type` parameter is used to specify the type of scaling to be used. In this case, we will use `standard`, which scales the data to have zero mean and unit variance.Other supported scalers are `minmax`, `robust`, `robust-iqr`, `minmax`, and `boxcox`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s, v_num=181, train_loss_step=0.413, train_loss_epoch=0.413]\n"
     ]
    }
   ],
   "source": [
    "nf = NeuralForecast(models=[model], freq='H', local_scaler_type='standard')\n",
    "nf.fit(df=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.b Forecast and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. The `Neuralforecast` class handles the inverse normalization, forecasts are returned in the original scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 26.56it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>TimesNet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>33.748502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>32.393269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>29.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>26.264737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>28.841827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ds   TimesNet\n",
       "unique_id                               \n",
       "BE        2016-11-01 00:00:00  33.748502\n",
       "BE        2016-11-01 01:00:00  32.393269\n",
       "BE        2016-11-01 02:00:00  29.000997\n",
       "BE        2016-11-01 03:00:00  26.264737\n",
       "BE        2016-11-01 04:00:00  28.841827"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_df = df[df['unique_id']=='FR'].tail(24*5).reset_index(drop=True)\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=False)\n",
    "Y_hat_df = Y_hat_df[Y_hat_df['unique_id']=='FR']\n",
    "\n",
    "plot_df = pd.concat([plot_df, Y_hat_df ]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plot_df[['y', 'TimesNet']].plot(linewidth=2)\n",
    "plt.axvline('2016-11-01', color='red')\n",
    "plt.ylabel('Price [EUR/MWh]', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "The inverse scaling is performed by the `Neuralforecast` class before returning the final forecasts. Therefore, the hyperparmater selection with `Auto` models and validation loss for early stopping or model selection are performed on the scaled data. Different types of scaling with the `Neuralforecast` class can't be automatically compared with `Auto` models.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Temporal Window normalization during training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temporal normalization scales each instance of the batch separately at the window level. It is performed at each training iteration for each window of the batch, for both target variable and temporal exogenous covariates. For more details, see [Olivares et al. (2023)](https://arxiv.org/abs/2305.07089) and https://nixtla.github.io/neuralforecast/common.scalers.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a. Instantiate model and `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temporal normalization is specified by the `scaler_type` argument. Currently, it is only supported for Windows-based models (`NHITS`, `NBEATS`, `MLP`, `TimesNet`, and all Transformers). In this example, we use the `TimesNet` model and `robust` scaler, recently proposed by Wu, Haixu, et al. (2022). First instantiate the model with the desired parameters.\n",
    "\n",
    "Visit https://nixtla.github.io/neuralforecast/common.scalers.html for a complete list of supported scalers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "model = TimesNet(h = horizon,                                                 # Horizon\n",
    "                 input_size = 5*horizon,                                      # Length of input window\n",
    "                 max_steps = 100,                                             # Training iterations\n",
    "                 top_k = 3,                                                   # Number of periods (for FFT).\n",
    "                 num_kernels = 3,                                             # Number of kernels for Inception module\n",
    "                 batch_size = 2,                                              # Number of time series per batch\n",
    "                 windows_batch_size = 32,                                     # Number of windows per batch\n",
    "                 learning_rate = 0.001,                                       # Learning rate\n",
    "                 futr_exog_list = ['gen_forecast', 'system_load','week_day'], # Future exogenous variables\n",
    "                 scaler_type = 'robust')                                      # Robust scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object and using the `fit` method. Note that `local_scaler_type` has `None` as default to avoid scaling the data before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  1.73it/s, v_num=183, train_loss_step=0.977, train_loss_epoch=0.977]\n"
     ]
    }
   ],
   "source": [
    "nf = NeuralForecast(models=[model], freq='H')\n",
    "nf.fit(df=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.b Forecast and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. The forecasts are returned in the original scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 20.26it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>TimesNet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>40.024895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>35.253803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>33.185341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>33.572426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>37.039207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ds   TimesNet\n",
       "unique_id                               \n",
       "BE        2016-11-01 00:00:00  40.024895\n",
       "BE        2016-11-01 01:00:00  35.253803\n",
       "BE        2016-11-01 02:00:00  33.185341\n",
       "BE        2016-11-01 03:00:00  33.572426\n",
       "BE        2016-11-01 04:00:00  37.039207"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_df = df[df['unique_id']=='FR'].tail(24*5).reset_index(drop=True)\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=False)\n",
    "Y_hat_df = Y_hat_df[Y_hat_df['unique_id']=='FR']\n",
    "\n",
    "plot_df = pd.concat([plot_df, Y_hat_df ]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plot_df[['y', 'TimesNet']].plot(linewidth=2)\n",
    "plt.axvline('2016-11-01', color='red')\n",
    "plt.ylabel('Price [EUR/MWh]', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "For most applications, models with temporal normalization (section 4) produced more accurate forecasts than time series scaling (section 3). However, with temporal normalization models lose the information of the relative level between different windows. In some cases this global information within time series is crucial, for instance when an exogenous variables contains the dosage of a medication. In these cases, time series scaling (section 3) is preferred.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski (2023). \"HINT: Hierarchical Mixture Networks For Coherent Probabilistic Forecasting\". International Conference on Machine Learning (ICML). Workshop on Structured Probabilistic Inference & Generative Modeling. Available at https://arxiv.org/abs/2305.07089.](https://arxiv.org/abs/2305.07089)\n",
    "- [Wu, Haixu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. \"Timesnet: Temporal 2d-variation modeling for general time series analysis.\", ICLR 2023](https://openreview.net/forum?id=ju_Uqw384Oq)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
